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A Scalable 32-Channel Neural Recording and Real-Time FPGA Based Spike Sorting System

Lookup NU author(s): Professor Andrew Jackson

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Abstract

© 2015 IEEE. This paper presents a scalable 32-channel neural recording platform with real-Time, on-node spike sorting capability. The hardware consists of: (1) an Intan Technologies RHD2132 neural amplifier; (2) a low power Igloo® nano Field Programmable Gate Array (FPGA) for spike detection and template matching; and (3) an EZ-USB FX3TM peripheral controller for transmitting the data to a computer over USB 3.0. The system is implemented in 2 parts, the first board (headstage, 51mm×33 mm) contains the RHD2132 and FPGA and is intended to be in close proximity to the animal, while the second (80mm×56 mm) is a combination of an FX3 development board with a custom interface shield. Graphical User Interfaces (GUI) for controlling the system, displaying real-Time raw & template matched data, and for template generation with WaveClus. Noise performance of below 1 LSB (∼3 μV) was measured and system power consumption (excluding FX3) of 15.1mW while idle, 34mW while outputting raw data and 34mW while outputting template matched results.


Publication metadata

Author(s): Williams I, Luan S, Jackson A, Constandinou TG

Publication type: Conference Proceedings (inc. Abstract)

Publication status: Published

Conference Name: IEEE Biomedical Circuits and Systems Conference: Engineering for Healthy Minds and Able Bodies, BioCAS 2015 - Proceedings

Year of Conference: 2015

Online publication date: 07/12/2015

Acceptance date: 01/01/1900

Publisher: Institute of Electrical and Electronics Engineers Inc.

URL: http://doi.org/ 10.1109/BioCAS.2015.7348330

DOI: 10.1109/BioCAS.2015.7348330

Library holdings: Search Newcastle University Library for this item

ISBN: 9781479972333


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